Data Structures and Algorithms

2504 Submissions

[4] viXra:2504.0152 [pdf] submitted on 2025-04-24 05:04:20

Basics of Indirect Programming

Authors: Olegs Verhodubs
Comments: 9 Pages.

The human worldview is firmly based on the notion that every action leads to a certain result. That is, in order to achieve a certain result, you need to perform some action. However, in the real world, which is always different from the model of the world in a person’s head, each action is followed by not only a result, but also some side effects. Practice shows that side effects can be used to achieve the goal no less effectively than the main actions. Often, using side effects, you can achieve even more than using the main actions. Side effects are a special case of indirect actions. It is indirect actions applicable to programming that will be considered in this paper. That is, indirect programming, namely the use of roundabout ways to achieve the goal of the program, is considered in this paper.
Category: Data Structures and Algorithms

[3] viXra:2504.0109 [pdf] submitted on 2025-04-16 09:26:39

Introduction to Complex Automata using Cook Algebra

Authors: Mirzakhmet Syzdykov
Comments: 1 Page.

As we have prior result of regular grammars over set of computational problems, we are to present the universal ‘complexity automata’ which can be used in solving any problem.
Category: Data Structures and Algorithms

[2] viXra:2504.0108 [pdf] submitted on 2025-04-16 10:15:41

Simple Algorithms to Solve MAX-SAT and TSP

Authors: Mirzakhmet Syzdykov
Comments: 1 Page.

We present the solution to two classical problems like MAX-SAT, or its 3-SAT partial case, and TSP from computational complexity theory using subset construction.
Category: Data Structures and Algorithms

[1] viXra:2504.0004 [pdf] submitted on 2025-04-01 20:49:31

Compression as Dimensional Projection: A Theoretical Framework Inspired by Golomb and Shannon

Authors: Vasant Jayasankar
Comments: 9 Pages. (Note by viXra Admin: Please submit article written with AI assistance to ai.viXra.org)

Modern compression theory is based on Shannon’s foundational insight that information is governed by entropy. Golomb coding, devised in the 1960’s, is a remarkably efficient solution for encoding geometrically distributed integers, and remains in widespread use because of its simplicity and effectiveness. In this paper, I revisit Golomb coding not merely as a mathematical transformation, but as a process of dimensional projection.I propose a generalized compression framework derived from the principles of dimensional projection. The process takes structured data and temporarily lifts it into higher-dimensional space to expose latent informational geometry, then systematically re-flattens it into a minimal entropy representation. Specifically, I reinterpret Golomb coding as a 1D-to-2D projection followed by structured reduction, and generalize this concept into a proposed 4D projection-based model applicable across multiple data types—text, DNA, images, audio, video, and more.The proposed framework provides a unifying geometric perspective on compression, revealing new axes of redundancy not captured by traditional frequency-based methods. This theoretical work offers a foundation for future research in entropy minimization, structural compression, and the dimensional nature of information itself.
Category: Data Structures and Algorithms